inclusion_BF {BayesTools} | R Documentation |

## Compute inclusion Bayes factors

### Description

Computes inclusion Bayes factors based on prior model probabilities, posterior model probabilities (or marginal likelihoods), and indicator whether the models represent the null or alternative hypothesis.

### Usage

```
inclusion_BF(prior_probs, post_probs, margliks, is_null)
```

### Arguments

`prior_probs` |
vector of prior model probabilities |

`post_probs` |
vector of posterior model probabilities |

`margliks` |
vector of marginal likelihoods. |

`is_null` |
logical vector of indicators whether the model corresponds to the null or alternative hypothesis (or an integer vector indexing models corresponding to the null hypothesis) |

### Details

Supplying `margliks`

as the input is preferred since it is better at dealing with
under/overflow (posterior probabilities are very close to either 0 or 1). In case that both the
`post_probs`

and `margliks`

are supplied, the results are based on `margliks`

.

### Value

`inclusion_BF`

returns a Bayes factor.

*BayesTools*version 0.2.17 Index]